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Hua-Peng Chen is Professor and Director of the Institute for Smart Transportation Infrastructure at East China Jiaotong University, China. He was formerly Professor of Civil Engineering and Head of Innovative and Smart Structures at the University of Greenwich, UK. He received his PhD in Structural Engineering from the University of Glasgow, UK. He has been working for over 20 years on structural health monitoring, advanced numerical modelling and structural performance assessment. He is a Chartered Civil Engineer (UK) and a Fellow of the Institution of Civil Engineers (UK).
Preface xiii
Biography xv
1 Introduction to Structural Health Monitoring 1
1.1 Advances in Structural Health Monitoring Technology 1
1.1.1 Structural Health in Civil Engineering 1
1.1.2 Aims of Structural Health Monitoring 2
1.1.3 Development of SHM Methods 3
1.2 Structural Health Monitoring System and Strategy 4
1.2.1 SHM System and its Components 4
1.2.2 SHM Strategy and Method 6
1.3 Potential Benefits of SHM in Civil Engineering 7
1.3.1 Character of SHM in Civil Engineering 7
1.3.2 Potential Benefits of SHM 9
1.4 Challenges and Further Work of SHM 10
1.4.1 Challenges of SHM in Civil Engineering 10
1.4.2 Further Work on SHM for Practical Applications 11
1.5 Concluding Remarks 13
References 13
2 Sensors and Sensing Technology for Structural Monitoring 15
2.1 Introduction 15
2.2 Sensor Types 16
2.3 Sensor Measurements in Structural Monitoring 21
2.3.1 Structural Responses 21
2.3.2 Environmental Quantities 24
2.3.3 Operational Quantities 25
2.3.4 Typical Quantities for Bridge Monitoring 25
2.3.5 Example of an SHM System-a Suspension Bridge (I) 27
2.4 Fibre Optic Sensors 33
2.4.1 Classification of Fibre Optic Sensors 33
2.4.2 Typical Fibre Optic Sensors in SHM 33
2.4.3 Fibre Optic Sensors for Structural Monitoring 36
2.5 Wireless Sensors 37
2.5.1 Components of Wireless Sensors 38
2.5.2 Field Deployment in Civil Infrastructure 39
2.6 Optimum Sensor Selection and Placement 39
2.6.1 Factors for Sensor Selection 40
2.6.2 Optimal Sensor Placement 41
2.7 Case Study 42
2.7.1 Sensors and Sensing System for SHM 43
2.7.2 Installation of FBG Sensors 43
2.8 Concluding Remarks 47
References 48
3 Data Acquisition, Transmission and Management 51
3.1 Introduction 51
3.2 Data Acquisition Systems 52
3.2.1 Data Acquisition for Structural Monitoring 52
3.2.2 Data Acquisition in Bridge Monitoring 53
3.3 Data Transmission Systems 54
3.3.1 Wired Transmission Systems 54
3.3.2 Wireless Transmission Systems 55
3.3.3 Data Transmission in Bridge Monitoring 56
3.4 Data Processing Systems 57
3.4.1 Data Pre-Processing for SHM 57
3.4.2 Data Analysis and Compression 58
3.4.3 Data Processing in Bridge Monitoring 58
3.5 Data Management Systems 59
3.5.1 Data Storage and File Management 59
3.5.2 Data Management in Bridge Monitoring 60
3.6 Case Study 61
3.7 Concluding Remarks 64
References 66
4 Structural Damage Identification Techniques 69
4.1 Introduction 69
4.2 Damage in Structures 70
4.3 Non-Destructive Testing Techniques 71
4.3.1 Acoustic Emission 72
4.3.2 Ultrasound 73
4.3.3 Guided (Lamb) Waves 74
4.3.4 Thermography 75
4.3.5 Electromagnetic Methods 76
4.3.6 Capacitive Methods 76
4.3.7 Laser Doppler Vibrometer 77
4.3.8 Global Positioning System 78
4.3.9 Visual Inspection 79
4.4 Comparison of NDT and SHM 79
4.5 Signal Processing for Damage Detection 81
4.5.1 Fourier Based Transforms 81
4.5.2 Wavelet Transforms 81
4.5.3 Hilbert-Huang Transform 83
4.5.4 Comparison of Various Transforms 84
4.6 Data-Based Versus Model-Based Techniques 84
4.7 Development of Vibration-Based Methods 87
4.8 Concluding Remarks 88
References 89
5 Modal Analysis of Civil Engineering Structures 91
5.1 Introduction 91
5.2 Basic Equations for Structural Dynamics 92
5.2.1 Modal Solution 93
5.2.2 Frequency Response Function 94
5.3 Input-Output Modal Identification 94
5.3.1 Equipment and Test Procedure 95
5.3.2 Modal Identification Techniques 96
5.3.2.1 Frequency-Domain Techniques 96
5.3.2.2 Time-Domain Techniques 96
5.3.3 Example for Modal Identification - a Steel Space Frame (I) 96
5.4 Output-Only Modal Identification 98
5.4.1 Equipment and Test Procedure 98
5.4.2 Operational Modal Identification Techniques 99
5.4.2.1 Frequency-Domain Methods 99
5.4.2.2 Time-Domain Methods 100
5.4.3 Damping Estimation 101
5.4.4 Effect of Temperature on Modal Data 101
5.4.5 Comparison of Methods 102
5.4.6 Example for Modal Identification - a Cable-Stayed Bridge 103
5.5 Correlation Between Test and Calculated Results 104
5.5.1 Modal Assurance Criterion 105
5.5.2 Orthogonality Checks 107
5.5.3 Modal Scale Factor 108
5.5.4 Coordinate Modal Assurance Criterion 108
5.6 Mode Shape Expansion and Model Reduction 109
5.6.1 General Expansion and Reduction Methods 109
5.6.2 Perturbed Force Approach 111
5.6.3 Comparison of Methods 112
5.7 Case Study 114
5.7.1 Operational Modal Analysis 115
5.7.2 Mode Shape Expansion 118
5.8 Concluding Remarks 118
References 120
6 Finite Element Model Updating 123
6.1 Introduction 123
6.2 Finite Element Modelling 125
6.2.1 Stiffness and Mass Matrices 125
6.2.2 Finite Element Modelling Error 125
6.3 Structural Parameters for Model Updating 126
6.3.1 Updating Parameters for Framed Structures 127
6.3.1.1 Updating Stiffness and Mass at Element Level 127
6.3.1.2 Updating Stiffness at Integration Point Level 127
6.3.1.3 Updating Material and Sectional Properties 128
6.3.1.4 Updating Joints and Boundary Conditions 128
6.3.2 Updating Parameters for Continuum Structures 128
6.4 Sensitivity Based Methods 129
6.4.1 Sensitivity Matrix 129
6.4.1.1 Sensitivity of Eigenvalue 130
6.4.1.2 Sensitivity of Eigenvector 130
6.4.1.3 Sensitivity of Input Force 131
6.4.2 Direct Parameter Estimation 131
6.4.3 Residual Minimisation Methods 132
6.4.4 Example for Model Updating - a Cantilever Beam 133
6.5 Dynamic Perturbation Method 135
6.5.1 Governing Equations 135
6.5.2 Regularised Solution Procedure 137
6.6 Use of Dynamic Perturbation Method for Model Updating 139
6.6.1 Use of Frequencies Only 139
6.6.2 Use of Incomplete Modes 140
6.6.2.1 Iterative Solution Method 142
6.6.2.2 Simplified Direct Solution Method 142
6.6.3 Example for Model Updating - a Plane Frame 143
6.6.4 Example for Model Updating - a Steel Space Frame (II) 145
6.7 Case Study 149
6.8 Concluding Remarks 151
References 153
7 Vibration-Based Damage Identification Methods 155
7.1 Introduction 155
7.2 Structural Modelling for Damage Identification 156
7.3 Methods Using Change of Modal Parameters 159
7.3.1 Natural Frequencies 159
7.3.2 Direct Mode Shape Comparison 160
7.3.3 Mode Shape Curvature 161
7.3.4 Damping 162
7.3.5 Frequency Response Function Curvature 162
7.3.6 Modal Strain Energy 163
7.3.7 Example for Damage Localisation - a Suspension Bridge (II) 165
7.4 Methods Using Change of Structural Parameters 169
7.4.1 Flexibility Matrix 169
7.4.2 Strain Energy Based Damage Index 172
7.4.3 Modal Strain-Based Damage Index 174
7.4.4 Example for Damage Localisation - a Suspension Bridge (III) 175
7.5 Pattern Recognition Methods 177
7.5.1 Stochastic Pattern Recognition 178
7.5.2 Novelty Detection 179
7.5.3 Example for Damage Detection - a Suspension Bridge (IV) 180
7.6 Neural Network Techniques 182
7.6.1 Back-Propagation Neural Network 182
7.6.2 Input Parameters and Pre-Processing 184
7.6.3 Probabilistic Neural Network 185
7.6.4 Example for Damage Localisation - a Suspension Bridge (V) 186
7.7 Concluding Remarks 189
References 190
8 Model-Based Damage Assessment Methods 195
8.1 Introduction 195
8.2 Characterisation of Damage in Structures 196
8.2.1 Damage in Framed Structures 197
8.2.1.1 Damage Characterisation at Element Level 197
8.2.1.2 Damage Characterisation at Critical Point Level 197
8.2.2 Damage in Continuum Structures 199
8.2.2.1 Damage Characterisation at Element Level 199
8.2.2.2 Damage Characterisation at Integration Point Level 199
8.3 Matrix Update Methods 200
8.3.1 Residual Force Vector Method 200
8.3.2 Minimum Rank Update Method 201
8.3.3 Optimal Matrix Updating Method 202
8.3.4 Example for Damage Assessment - a Plane Truss 203
8.4 Sensitivity Based Methods 204
8.4.1 Eigen-Parameter Sensitivity Method 204
8.4.2 FRF Sensitivity Method 205
8.4.3 Example for Damage Assessment - a Grid Structure 207
8.5 Damage Assessment Using Dynamic Perturbation Method 207
8.5.1 Use of Frequencies Only 208
8.5.2 Use of Incomplete Modes 209
8.5.3 Examples for Damage Assessment - Simple Framed Structures 211
8.5.3.1 Damage Assessment of a Grid Structure Using Frequencies Only 211
8.5.3.2 Damage Assessment of a Plane Truss Using Incomplete Modes 212
8.6 Numerical Examples 213
8.6.1 Framed Building Structure 213
8.6.2 Gravity Dam Structure 218
8.7 Potential Problems in Vibration-Based Damage Identification 220
8.7.1 Finite Element Model and Experimental Data 220
8.7.2 Effect of Modelling and Measurement Errors 221
8.7.3 Effect of Environmental Factors 222
8.7.4 Frequency Range and Damage Detectability 222
8.7.5 Damage Diagnosis and Prognosis 223
8.8 Concluding Remarks 224
References 225
9 Monitoring Based Reliability Analysis and Damage Prognosis 227
9.1 Introduction 227
9.2 Usage Monitoring 229
9.2.1 Lifecycle Monitoring 229
9.2.2 Load Monitoring and Evaluation 230
9.2.3 Monitoring of Environmental Factors 231
9.2.4 Example for Usage Monitoring - a Suspension Bridge (VI) 233
9.3 Probabilistic Deterioration Modelling 235
9.3.1 Sources of Deterioration 235
9.3.2 Modelling and Parameter Uncertainty 236
9.3.3 Probabilistic Deterioration Models 237
9.3.3.1 Failure Rate Function 237
9.3.3.2 Markov Process 237
9.3.3.3 Gamma Process 238
9.3.4 Example for Fatigue Cracking Modelling - a Steel Bridge (I) 239
9.4 Lifetime Distribution Analysis 240
9.4.1 Stochastic Gamma Process 240
9.4.2 Weibull Life Distribution Model 241
9.4.3 Data Informed Updating 242
9.4.4 Example for Lifetime Distribution Analysis - a Concrete Bridge 243
9.5 Structural Reliability Analysis 244
9.5.1 Limit States and Reliability Analysis 245
9.5.2 Time-Variant Reliability 247
9.5.3 Remaining Useful Life 248
9.5.4 Example for Fatigue Reliability Analysis - a Suspension Bridge (VII) 248
9.6 Optimum Maintenance Strategy 250
9.6.1 Lifetime Costs 251
9.6.2 Decision Based on Lifetime Deterioration 253
9.6.2.1 Failure Rate Function Model 253
9.6.2.2 Markov Process Model 253
9.6.2.3 Gamma Process Model 254
9.6.2.4 Survival Function 254
9.6.3 Decision Based on Structural Reliability 255
9.6.4 Example for Optimal Maintenance - a Steel Bridge (II) 256
9.7 Case Study 256
9.7.1 Traffic Loads Monitoring 257
9.7.2 Cable Force Monitoring 260
9.7.3 Stiffening Deck System Stress Monitoring 261
9.8 Concluding Remarks 263
References 264
10 Applications of SHM Strategies to Large Civil Structures 267
10.1 Introduction 267
10.2 SHM System and Damage Identification of a Cable-Stayed Bridge 268
10.2.1 Sensors and Sensing Network 268
10.2.2 Data Management System 270
10.2.3 Operational Modal Analysis and Mode Identifiability 270
10.2.4 Finite Element Modelling 271
10.2.5 Damage Localisation Using Mode Shape Curvature Index 273
10.2.6 Damage Detection Using Neural Network 275
10.3 In-Construction Monitoring of a High-Rise Building 277
10.3.1 Long-Term SHM System 278
10.3.2 Monitoring During Shoring Dismantlement 279
10.3.3 Wireless Sensing Network for Vibration Monitoring 280
10.3.4 Ambient Vibration Tests and Results 282
10.4 Monitoring of Tunnel Construction Using FBG Sensors 284
10.4.1 Temperature Monitoring of Tunnel Cross Passage Construction 284
10.4.2 Settlement Monitoring of Undercrossing Tunnel Construction 287
10.5 Safety Monitoring of Rail Using Acoustic Emission 288
10.5.1 Rail Track Damage Detection System 289
10.5.2 On-Site Monitoring Data 290
10.6 Structural Integrity Monitoring of Water Mains 294
10.6.1 FBG Sensory System 294
10.6.2 Implementation of Monitoring System 296
10.6.3 Measurements Under Different Operational Conditions 296
10.7 Concluding Remarks 301
References 302
Index 303
Structural Health Monitoring (SHM) is a process of in-service health assessment for a structure through an automated monitoring system, and it is a key element of cost-effective strategies for condition-based maintenance. A SHM strategy consists of many important components including sensing network, data processing and analysis, damage assessment and decision making. SHM technology has the great potential to offer significant economic and life-safety benefits. However, the application of the SHM technology to actual civil engineering structures is still in its infancy, and it requires advancements in various fields due to its multi-disciplinary nature. Extensive further works are therefore needed to ensure that infrastructure managers benefit from this emerging technology. This chapter first introduces the development of SHM technology and the framework and strategy of SHM systems. The critical issues and potential benefits of the application of SHM to large civil engineering structures are presented. Finally, the challenges of SHM technologies in civil engineering applications and the required further studies are discussed.
The structural health monitoring process involves the observation and evaluation of a structure over time using periodically sampled measurements from a sensing system. Structural health monitoring is a popular and growing research field, providing a powerful tool for damage assessment and performance evaluation of engineering structures.
Civil infrastructure comprises bridges, buildings, towers, pipelines, tunnels, dams and other types of structures. Their continued safe and economical operation largely depends on proper maintenance and management. In order to evaluate optimal management strategies for existing civil infrastructure, accurate assessment of present and future safety is important and necessary (Ettouney and Alampalli 2012). Maintaining safe and reliable civil infrastructure for daily use is critical to the well-being of the society. Thus, structural health can be stated as its current capacity for providing intended level of service in a safe and cost-effective manner against the expected hazards during its service life.
Despite the necessary design methodology initially used, civil engineering structures deteriorate with time. This deterioration is due to various reasons, including failure caused by cyclic traffic loads, effects of environmental factors (e.g. steel corrosion, concrete carbonation) and aging in the construction materials. Also, the deterioration can be caused by infrequent extreme events such as earthquakes, hurricanes and floods. Therefore, structural health will be affected by operational and environmental factors, including normal load conditions, current and future environments and expected hazards during the lifetime. All these factors are variables with uncertainties, so it is difficult to define the structural health in terms of its age and usage and its level of safety to resist severe natural actions. In order to reliably assess structural health and maintain structural safety, continued in-service monitoring of the structure is essential.
Catastrophic structural failures, such as sudden collapse of the I-35 highway bridge (NTSB 2008), have highlighted problems associated with aging critical civil infrastructure. Severe natural disasters such as earthquakes and typhoons result in demands for quick condition assessment of civil structures (Brownjohn et al. 2011). Currently, the condition assessment of existing civil infrastructure such as bridges largely depends on visual inspection. This subjective and inaccurate condition assessment methodology has been identified as the most critical technical barrier to effective infrastructure management. For example, condition of bridges is typically expressed in terms of subjective indices on the basis of visual inspection alone. Thus, it is difficult to accurately evaluate structural condition from the inaccurate visual inspection data, even when this may be conducted by experts (Aktan et al. 1998). These issues have driven the research and development on the continuous observation and interpretation of full-scale performance of civil engineering structures during their service life.
Health monitoring applications based on advanced sensors and real-time monitoring for civil infrastructure offer great potential for informed and effective infrastructure management. Health monitoring is necessary for civil engineering structures since they may exhibit premature deterioration, structural damage and performance problems, or they may even have aged beyond their expected design life. Health monitoring can be utilised for tracking the responses of a structure along with inputs, if possible, over a sufficient duration to determine anomalies, to detect deterioration and to assess damage for decision making. Damage assessment methods using measured vibration modal data, such as natural frequencies and mode shapes, show promise for the health evaluation of engineering structures (Bicanic and Chen 1997, Chen 1998). Health monitoring can assess the performance of civil structures in a proactive manner using measured data and data interpretation algorithms, in order to correctly evaluate the current condition and to predict the remaining service life.
Structural health monitoring is defined as the process of implementing a damage identification and health evaluation strategy for engineering structures. SHM uses sensing systems and associated hardware and software facilities to monitor the structural performance and operational environments of engineering structures. SHM involves the observation of a structure over time, using periodically sampled structural response and operational environment measurements from an array of sensors and then the evaluation of the current state and future performance of the structure. For long-term SHM, the output of this process is periodically updated information regarding the capability of the structure to perform its intended function, by considering the inevitable aging and degradation resulting from operational environments (Farrar et al. 2003). Furthermore, SHM is adopted for rapid condition assessment to provide prompt and reliable information regarding the integrity of the structure after extreme events, such as an earthquake or blast loading.
SHM aims to identify structural damage and evaluate the health of the structure using monitored data. Damage is defined here as changes to the material and/or geometric properties of a structure, which affects the current state and future performance of the structure. The objectives of an SHM strategy can be outlined as the following five levels (Farrar et al. 2009).
The level in the order given above represents increasing knowledge of the damage state. A higher level usually requires information available about all lower levels. The first two levels, damage detection and localisation, can be generally achieved using vibration based damage detection methods from structural dynamic response measurements. To identify the type of damage, data from structures with the specific types of damage must be available for correlation with the measured data. Analytical models are usually needed to achieve the fourth and fifth levels, damage assessment and prognosis. In general, these two levels may not be achieved without first identifying the type of damage present. Estimates of the future loading, together with predictive deterioration models, are necessary to accomplish the final level for damage prognosis.
SHM strategies offer useful information for optimising maintenance planning of engineering structures in service. To ensure a reliable operation and to schedule maintenance and repair work in a cost-effective manner, it is necessary to continuously monitor and assess the structural performance and to have an accurate estimation of the remaining useful life. Thus, the SHM strategy integrated with lifecycle management is necessary to calibrate structural assessment and predictions, to enable optimal operation and maintenance of engineering structures and, eventually, to operate the structures beyond their original design life.
Structural damage identification based on changes in the dynamic response of the structure has been practised in a qualitative manner for a long time. The beginnings of this damage detection method as an area of interest to engineers can be traced back as far as the time when tap-testing (e.g. on train wheels) for fault detection became common. This field, however, did not really become established in research communities until the 1980s, when much interest was generated in the structural condition of offshore platforms, and later in the health of aerospace structures. Recently, the development of quantifiable SHM methods has been closely linked with the evolution...
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